Traceable by Design: An LLM Pipeline and Dashboard for EU Regulatory Consultation Analysis

Abstract:Public consultations generate large volumes of data in the form of stakeholder submissions that are practically unfeasible to analyse manually. We present an end-to-end LLM-based pipeline and interactive dashboard for structured topic extraction from regulatory consultation submissions, demonstrated on the European Commission's Digital Fairness Act (DFA) public call for evidence as a case study. The system processes raw PDF attachments and web-form responses, extracts topic annotations, and grounds every extraction in a verbatim quote from the source text. Applied to 4,322 DFA submissions, the pipeline produced 15,368 topic annotations supported by 20,951 verbatim evidence quotes. Three principles govern the proposed design: verbatim grounding, full traceability, and transparency by design. The dashboard exposes the full extraction dataset through five analytical views, from dataset-level topic overviews to individual paragraph drill-downs, with every result traceable to its source. Beyond the predefined DFA topic categories, the pipeline generated certain stakeholder concerns, such as Age Verification, Payment Processor Censorship, and Digital Ownership, that a fixed-taxonomy approach would have missed. The pipeline is domain-generic; adapting it to a new consultation requires only a prompt update and a new dataset. A live demo is available at this https URL. The code and processed data are publicly available at this https URL.

Computers and Society (cs.CY); Computation and Language (cs.CL)

arXiv:2605.30995 [cs.CY]

(or arXiv:2605.30995v2 [cs.CY] for this version)

https://doi.org/10.48550/arXiv.2605.30995 Focus to learn more arXiv-issued DOI via DataCite

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